The training signals were paired with each other to form training samples for the Siamese model. As we have too many mismatched samoles in comparison to matched samples, we used Synthetic Minority Oversampling Technique (SMOTE) [1]} to generate synthetic data for matching cases. 21.5 times synthetic samples were generated for each person as a trade-off between data imbalance and computation requirements. This enables the model to learn which patterns are discriminative of matching cases. 80% of the signal pairs were used to train the model and the rest were used as validation data. Since we predict a continuous variable as the outcome in this model the Adam optimizer [2]} was used to minimize the Mean squared error (MSE) loss. The model was trained for 75 epochs to predict `0' for mismatches and `1' for matches.
